{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets#包括各种数据集\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
      "(150,)\n",
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "iris=datasets.load_iris()#加载鸢尾花数据集\n",
    "# print(iris.DESCR)#数据集文档\n",
    "print(iris.data.shape)#数据规格\n",
    "print(iris.feature_names)#特征名称\n",
    "print(iris.target.shape)#150个待分类的数据（150个向量）\n",
    "print(iris.target_names)#三种鸢尾花品种\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target#150个数据带标签，分为0，1，2分别对应三个种类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.data.shape#150行，4列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17d144e3190>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=iris.data[:,:2]#150个数据，取前两个字段\n",
    "plt.scatter(x[:,0],x[:,1])#散点图，取第一个字段为X轴，第二个为Y轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#萼片长宽\n",
    "x=iris.data[:,:2]#150个数据，取前两个字段\n",
    "Y=iris.target#三种类型的150个列表（带标签0,1,2）\n",
    "plt.scatter(x[Y==0,0],x[Y==0,1],color=\"red\",marker=\"o\")#第一种鸢尾花的前两个特征分布\n",
    "plt.scatter(x[Y==1,0],x[Y==1,1],color=\"green\",marker=\"x\")#第二种鸢尾花的前两个特征分布\n",
    "plt.scatter(x[Y==2,0],x[Y==2,1],color=\"blue\",marker=\"+\")#第三种鸢尾花的前两个特征分布\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#花瓣长宽\n",
    "X=iris.data[:,2:]#取第3，4个特征\n",
    "Y=iris.target#三种类型的150个列表（带标签0,1,2）\n",
    "plt.scatter(X[Y==0,0],X[Y==0,1],color=\"red\",marker=\"o\")#第一种鸢尾花的3，4特征分布\n",
    "plt.scatter(X[Y==1,0],X[Y==1,1],color=\"green\",marker=\"x\")#第二种鸢尾花的3，4特征分布\n",
    "plt.scatter(X[Y==2,0],X[Y==2,1],color=\"blue\",marker=\"+\")#第三种鸢尾花的3，4特征分布\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
